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Mathematics
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Optimization and Path Planning of Robotics
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Optimization and Path Planning of Robotics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 439

Special Issue Editor

Dr. Enrique Garcia

E-Mail Website
Guest Editor
Instituto Politécnico Nacional ESIME Azcapotzalco, Tecnológico de Estudios Superiores de Huixquilucan, Estado de México 52773, Mexico
Interests: trajectory planning; machine learning for robotics; robotics control

Special Issue Information

Dear Colleagues,

Optimization and path planning constitute critical components of robotics that significantly enhance the efficiency, safety, and overall performance of real-world applications. These mathematical methodologies enable robotic systems to navigate complex environments, avoid obstacles, and execute tasks with precision and minimal resource utilization. This not only improves their operational effectiveness, but also extends their capabilities in various applications, ranging from industrial automation to autonomous vehicles and space exploration. As the field of robotics continues to advance and integrate into diverse domains, the development of sophisticated optimization and path planning methods becomes increasingly imperative for creating more intelligent, versatile, and reliable robotic solutions. 

This Special Issue aims to present innovative mathematical solutions addressing challenges in robotic optimization and path planning, with a particular focus on optimization techniques for robot motion, state-of-the-art path planning algorithms, collision avoidance strategies, simulation, and mathematical techniques for enhancing robot performance and navigation. 

This Special Issue welcomes submissions of exceptional quality, including original research papers and review articles of outstanding merit in the following areas:

  • Optimization methods;
  • Path planning algorithms;
  • Collision avoidance strategies;
  • Machine learning approaches;
  • Computational geometry;
  • Robot motion simulation;
  • Real-time path planning;
  • Swarm robotics and distributed optimization

Dr. Enrique Garcia
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optimization
  • path planning
  • collision avoidance
  • computational geometry
  • simulation

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Published Papers (1 paper)

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Research

20 pages, 26297 KiB  
Article
A Framework for Coverage Path Planning of Outdoor Sweeping Robots Deployed in Large Environments
by Braulio Félix Gómez, Akhil Jayadeep, M. A. Viraj J. Muthugala and Mohan Rajesh Elara
Mathematics 2025, 13(14), 2238; https://doi.org/10.3390/math13142238 - 10 Jul 2025
Viewed by 264
Abstract
Outdoor sweeping is a tedious and labor-intensive task essential for maintaining the cleanliness of public spaces such as gardens and parks. Robots have been developed to address the limitations of traditional methods. Coverage Path Planning (CPP) is a critical function for these robots. [...] Read more.
Outdoor sweeping is a tedious and labor-intensive task essential for maintaining the cleanliness of public spaces such as gardens and parks. Robots have been developed to address the limitations of traditional methods. Coverage Path Planning (CPP) is a critical function for these robots. However, existing CPP methods often perform poorly in large environments, where such robots are typically deployed. This paper proposes a novel CPP framework for outdoor sweeping robots operating in expansive outdoor areas, defined as environments exceeding 1000 square meters in size. The framework begins by decomposing the environment into smaller sub-regions. The sequence in which these sub-regions are visited is then optimized by formulating the problem as a Travelling Salesman Problem (TSP), aiming to minimize travel distance. Once the visiting sequence is determined, a boustrophedon-based CPP is applied within each sub-region. We analyzed two decomposition strategies, Voronoi-based and grid-based, and evaluated three TSP optimization techniques: local search, record-to-record travel, and simulated annealing. This results in six possible combinations. Simulation results demonstrated that Voronoi-based decomposition achieves higher area coverage (average coverage of 95.6%) than grid-based decomposition (average coverage 52.8%). For Voronoi-based methods, local search yielded the shortest computation time, while simulated annealing achieved the lowest travel distance. We have also conducted hardware experiments to validate the real-world applicability of the proposed framework for efficient CPP in outdoor sweeping robots. The robot hardware experiment achieved 84% coverage in a 19 m × 17 m environment. Full article
(This article belongs to the Special Issue Optimization and Path Planning of Robotics)
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